| Project ID |
BITS-SRIP/E0BDDC/2026 |
| Project Title |
Automated Research Gap Discovery and Topic Recommendation Using Large Language Models |
| Project Description |
Automated Research Gap Discovery and Topic Recommendation Using Large Language Models
Current literature reviews are largely manual, time-intensive, subjective, and non-scalable, often resulting in duplicated research efforts or poorly justified problem statements. Despite recent advances in Large Language Models (LLMs), their potential to automate research gap identification and research topic generation remains largely unexplored.
Objectives include:
• Automatically extract structured research contributions and limitations from published papers.
• Identify, normalize, and categorize research gaps using semantic analysis.
• Visually map the evolution and coverage of research work over time.
• Generate high-quality, literature-grounded research topics from identified gaps
The expected outcome (in MVP format)
• Automated Research Gap Knowledge Base: A structured repository of research gaps extracted from 10 years of literature.
• Visual Research Landscape: Pictorial representations of research trends, saturation points, and under-explored areas.
• Research Topic Recommendations: Five novel, feasible, and high-impact research topics grounded in identified gaps.
• Reusable AI Framework: A scalable tool applicable to other research domains beyond solar forecasting.
Please note, ‘Solar forecasting’ will serve as the demonstration domain, with the framework designed to be domain-agnostic. |
| Project Discipline |
Student can come from any discipline with following skill set - python, Language model, NLP, knowledge of API |
| Faculty Name |
Pabitra Biswas |
| Department |
Department of Management |
